Estimating a distribution in a smooth fashion.
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#include <yat/statistics/Smoother.h>
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| Smoother (const regression::Kernel &, double width, const std::vector< double > &values) |
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| Smoother (const regression::Kernel &kernel, double width, double xmin, double xmax, size_t n) |
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void | add (double x, double weight=1.0) |
| Add a data point.
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const std::vector< double > & | density (void) const |
| estimated values
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void | rescale (double factor) |
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void | reset (void) |
| reset density to zero
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const std::vector< double > & | value (void) const |
| values in which distribution is estimated
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(Note that these are not member functions.)
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template<typename ForwardIterator > |
void | add (Smoother &h, ForwardIterator first, ForwardIterator last) |
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std::ostream & | operator<< (std::ostream &s, const Smoother &) |
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Estimating a distribution in a smooth fashion.
- Since
- New in yat 0.5
theplu::yat::statistics::Smoother::Smoother |
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const regression::Kernel & |
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double |
width, |
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const std::vector< double > & |
values |
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Constructor taking vector describing for which values distribution should be estimated.
- Note
- if values are not sorted the behavior is undefined
theplu::yat::statistics::Smoother::Smoother |
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const regression::Kernel & |
kernel, |
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double |
width, |
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double |
xmin, |
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double |
xmax, |
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size_t |
n |
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Constructor creating observation points equally distributed between xmin and xmax.
- Parameters
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kernel | doing the smoothing |
width | |
xmin | smallest observation point |
xmax | largest observation point |
n | number of observation points |
void theplu::yat::statistics::Smoother::rescale |
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double |
factor | ) |
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multiply each value with factor
- Since
- New in yat 0.6
template<typename ForwardIterator >
void add |
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Smoother & |
h, |
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ForwardIterator |
first, |
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ForwardIterator |
last |
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related |
Add a range [first, last) of values to Smoother.
std::ostream & operator<< |
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std::ostream & |
s, |
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const Smoother & |
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related |
The documentation for this class was generated from the following file: